Autonomy for the world. Backed by a16z.
About the role
Own day-to-day configuration of AI platforms and orchestration tools (models, routes, guardrails, tenants, policies, role mappings, prompt libraries, etc.). Design, configure, and maintain connectors and extensions into SaaS systems, data sources, and workflow tools; ensure connectivity is reliable, secure, and aligned with access policies.
What they're looking for
- · 5–7+ years in roles such as platform engineer, DevOps/SRE, ML/AI operations, or technical SaaS operations, with hands-on responsibility for production systems
- · Strong fluency in APIs, integrations, and infrastructure-as-config concepts, able to work in code/JSON/YAML configuration environments and with automation where appropriate
- · Hands-on experience with monitoring and observability tools (logs, metrics, alerts) and using them to diagnose issues and guide improvements
- · Practical experience working with at least one class of AI or automation platforms (LLM providers, AI productivity tools, RPA/workflow engines, or similar)
- · Comfort with secure secrets management and access control practices (roles, permissions, key rotation, least-privilege patterns)
- · Ability to document technical work and decisions clearly for both technical and non-technical audiences
More about this role
Shield AI is a venture-backed defense-tech company with the mission of protecting service members and civilians with intelligent systems. Its products include Hivemind autonomy software, V-BAT and X-BAT aircraft, and Aechelon simulation and synthetic reality technologies. With offices and facilities across the U.S., Europe, the Middle East, and Asia-Pacific, Shield AI’s technology actively supports operations worldwide. For more information, visit www.shield.ai . Follow Shield AI on LinkedIn , X , Instagram , and YouTube .
This is a deeply technical individual contributor role reporting to the Head of AI Operations & Governance (Enterprise AI). This person will own significant portions of the technical operating load for workplace AI: platform configuration, connector and integration management, observability wiring, secrets and access hygiene, model/prompt lifecycle mechanics, and hands-on changes in production.
At the same time, the role will help translate production realities into governance artifacts, risk assessments, status updates, and training/enablement support — giving the Head a force multiplier who can operate at both the technical and “softer” layers of AI operations...
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